Riolo Giulia, Cantara Silvia, Marzocchi Carlotta, Ricci Claudia
Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.
Methods Protoc. 2020 Dec 24;4(1):1. doi: 10.3390/mps4010001.
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.
微小RNA（miRNA）是动植物基因表达的转录后调节因子。通过与靶mRNA上的微小RNA反应元件（mRE）配对，miRNA发挥基因调节作用，在多个生理和病理过程中产生显著变化。因此，鉴定miRNA与mRNA的靶标相互作用对于发现由miRNA调控的网络至关重要。实现这一目标的最佳方法通常是通过计算预测，然后对这些miRNA与mRNA的相互作用进行实验验证。本综述总结了miRNA靶标鉴定的关键策略。存在几种用于计算分析的工具，每种工具都有不同的预测miRNA靶标的方法，并且其数量在不断增加。讨论了可用于此目的的主要算法，包括机器学习方法，以提供熟悉其假设并理解如何解释结果的实用提示。然后，描述了验证所鉴定的miRNA与mRNA靶标对真实性的所有实验程序，包括高通量技术，以便找到验证miRNA的最佳方法。对于每种策略，都讨论了其优缺点，以便用户能够根据自己的兴趣评估并选择正确的方法。
